Reservoir computing is a neuromorphic architecture based on artificial neural networks. It has gathered significant attention due to its simplicity and efficiency in processing complex sequential data for real-world tasks. We propose an advanced optoelectronic reservoir computing system that uses a single nonlinear node comprised of a Mach-Zehnder interferometer, an optical delay line, and several high-bandwidth integrated optoelectronic components. This system shows efficient performance on benchmark tasks such as signal recognition with an accuracy of 100%, nonlinear channel equalization for generating reconstructed signals with symbol error rates of 10−55, and time-series predictions that reach normalized mean square errors in the order of 10−2.

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Neuromorphic engineering,Time series predictions,Signal classification,Signal reconstruction,Reservoir computing

• Physical Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
• Materials Engineering - Engineering and Technology